R.Version()$platform
[1] "aarch64-apple-darwin20"
$arch
[1] "aarch64"
$os
[1] "darwin20"
$system
[1] "aarch64, darwin20"
$status
[1] ""
$major
[1] "4"
$minor
[1] "2.2"
$year
[1] "2022"
$month
[1] "10"
$day
[1] "31"
$`svn rev`
[1] "83211"
$language
[1] "R"
$version.string
[1] "R version 4.2.2 (2022-10-31)"
$nickname
[1] "Innocent and Trusting"The data was assessed with the formr survey framework (Arslan, Walther, and Tata 2020). The raw data was imported via the following code.
Please note that the results of the Bayesian analyses may slightly vary due to the estimation approach.
library(tidyverse)
library(ggforce)
library(lme4)
library(BFpack)
library(hrbrthemes)
library(patchwork)
library(brms)
library(viridis)
library(ggdist)
library(tidybayes)
library(here)
library(skimr)
load(here("data/teachers_study1_N40.RData"))
skim(study1)
set.seed(25051982)# wrangle information on the plot type, ES, ...
plot_info <- study1 %>%
pivot_longer(2:195, names_to = "variables", values_to = "values",
values_transform = as.character) %>%
dplyr::filter(str_detect(variables, "plot")) %>%
# we only need the rows with info on plots
tidyr::separate(col = values, into = c("type", "axis", "effsize"),
# separate the info into three columns
sep = "_", remove = F) %>%
dplyr::mutate(plot = variables, # rename variables for later join
type = paste(type, axis, sep = "_")) %>%
dplyr::select(-variables, -axis)
# wrangle answers to items on each page
item_values <- study1 %>%
dplyr::select(-c(topic:itemo)) %>%
pivot_longer(2:169, names_to = "variables", values_to = "values",
values_transform = as.character) %>%
dplyr::mutate(variables = case_when( # recode variable names that have
variables == "sensi_6" ~ "sensi_06", # accidentally been labeled
variables == "acccl_6" ~ "acccl_06", # without zero
variables == "accu3_6" ~ "accu3_06",
variables == "accov_6" ~ "accov_06",
variables == "diffi_6" ~ "diffi_06",
variables == "infor_6" ~ "infor_06",
variables == "value_6" ~ "value_06",
TRUE ~ variables
)) %>%
dplyr::mutate(plot = paste0("plotx_", str_sub(variables, -2, -1)),
# create variable for later join
variables = str_sub(variables, 1, -4)) %>%
# rename variable names to get a data set
# with one line per participant per page
pivot_wider(id_cols = c(session, plot), names_from = "variables",
values_from = "values")
# join the two data sets
study1_w <- full_join(plot_info, item_values,
by = c("session", "plot")) %>%
# by participant and page (plot)
dplyr::select(-values) %>%
dplyr::mutate(rating_cl = as.numeric(acccl), # some var need to be defined as
rating_u3 = as.numeric(accu3), # numeric again
rating_ov = as.numeric(accov),
diffi = as.numeric(diffi),
infor = as.numeric(infor),
value = as.numeric(value),
effsize = as.numeric(effsize),
effsize_cl = case_when(
# there is no negative Cliff's Delta, so we have to compute
# two transformations
effsize > 0 ~ (((2*pnorm(effsize/2))-1)/pnorm(effsize/2)),
# transform the actual effect size Cohen's d to Cliff's Delta
effsize < 0 ~ (- (((2*pnorm(abs(effsize)/2))-1)/pnorm(abs(effsize)/2)))
# transform the actual effect size Cohen's d to Cliff's Delta
# and make it negative as in the item
),
effsize_u3 = 1-pnorm(effsize), # reverse so that it fits the direction of the U3 item
# transform the actual effect size Cohen's d to Cohen's U3
effsize_ov = 2 * pnorm(-abs(effsize) / 2),
# transform the actual effect size Cohen's d to overlap
# actual difference of rating relative to depicted effectsize
diff_cl = (rating_cl - effsize_cl)/2,
# actual difference of rating relative to depicted effectsize
diff_u3 = (rating_u3/100) - effsize_u3,
# actual difference of rating relative to depicted effectsize
diff_ov = (rating_ov/100) - effsize_ov,
diffi_normed = ((diffi - 1) / 3) - 1, # transform item to -1 to 1
infor_normed = ((infor - 1) / 3) - 1, # transform item to -1 to 1
value_normed = ((value - 1) / 3) - 1) %>% # transform item to -1 to 1
group_by(session) %>%
mutate(rating_ov_missconcept = median(rating_ov, na.rm = T) < 68.9,
rating_u3_missconcept = median(rating_u3, na.rm = T) < 21.2) %>%
ungroup() %>%
mutate(rating_u3_filtered = ifelse(rating_u3_missconcept == T, NA, rating_u3),
rating_ov_filtered = ifelse(rating_ov_missconcept == T, NA, rating_ov),
diff_u3_filtered = (rating_u3_filtered/100) - effsize_u3,
diff_ov_filtered = (rating_ov_filtered/100) - effsize_ov,
sensi_binary = ifelse(is.na(sensi), # 1 if NOT "equal"
NA,
as.numeric(!grepl("equal", sensi))),
sensi_ordinal = ordered(factor(substr(sensi, 55, 100)),
levels = c("inferior",
"equal",
"superior")),
sensi_binary_filtered = case_when(sensi_ordinal == "equal" ~ 0,
(sensi_ordinal == "inferior" &
effsize < 0) |
(sensi_ordinal == "superior" &
effsize > 0) ~ as.numeric(NA),
(sensi_ordinal == "inferior" & # was not there
effsize > 0) |
(sensi_ordinal == "superior" &
effsize < 0) ~ 1,
TRUE ~ as.numeric(NA)), # was 1
sensi_correct = case_when(sensi_ordinal == "equal" ~ "judged equal",
(sensi_ordinal == "inferior" &
effsize < 0) |
(sensi_ordinal == "superior" &
effsize > 0) ~ "wrong direction",
(sensi_ordinal == "inferior" & # was not there
effsize > 0) |
(sensi_ordinal == "superior" &
effsize < 0) ~ "right direction",
TRUE ~ NA_character_),
effsize_abs = abs(effsize))
skim(study1_w)| Name | study1_w |
| Number of rows | 960 |
| Number of columns | 34 |
| _______________________ | |
| Column type frequency: | |
| character | 8 |
| factor | 1 |
| logical | 2 |
| numeric | 23 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| session | 0 | 1.0 | 64 | 64 | 0 | 40 | 0 |
| type | 0 | 1.0 | 13 | 19 | 0 | 4 | 0 |
| plot | 0 | 1.0 | 8 | 8 | 0 | 24 | 0 |
| sensi | 480 | 0.5 | 59 | 62 | 0 | 3 | 0 |
| acccl | 480 | 0.5 | 1 | 4 | 0 | 11 | 0 |
| accu3 | 480 | 0.5 | 1 | 3 | 0 | 48 | 0 |
| accov | 480 | 0.5 | 1 | 3 | 0 | 52 | 0 |
| sensi_correct | 480 | 0.5 | 12 | 15 | 0 | 3 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| sensi_ordinal | 480 | 0.5 | TRUE | 3 | sup: 177, inf: 159, equ: 144 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| rating_ov_missconcept | 0 | 1 | 0.22 | FAL: 744, TRU: 216 |
| rating_u3_missconcept | 0 | 1 | 0.55 | TRU: 528, FAL: 432 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| effsize | 0 | 1.00 | 0.00 | 0.56 | -0.80 | -0.50 | 0.00 | 0.50 | 0.80 | ▇▃▁▃▇ |
| diffi | 0 | 1.00 | 4.05 | 1.71 | 1.00 | 3.00 | 4.00 | 5.00 | 7.00 | ▇▆▆▆▇ |
| infor | 0 | 1.00 | 4.11 | 1.45 | 1.00 | 3.00 | 4.00 | 5.00 | 7.00 | ▅▅▇▇▅ |
| value | 0 | 1.00 | 4.13 | 1.49 | 1.00 | 3.00 | 4.00 | 5.00 | 7.00 | ▅▃▆▇▅ |
| rating_cl | 480 | 0.50 | 0.01 | 0.38 | -1.00 | -0.20 | 0.00 | 0.20 | 1.00 | ▂▆▇▃▁ |
| rating_u3 | 480 | 0.50 | 26.17 | 22.60 | 0.00 | 6.00 | 18.00 | 45.00 | 100.00 | ▇▃▃▁▁ |
| rating_ov | 480 | 0.50 | 71.63 | 28.63 | 0.00 | 65.00 | 80.00 | 90.00 | 100.00 | ▂▁▁▅▇ |
| effsize_cl | 0 | 1.00 | 0.00 | 0.34 | -0.47 | -0.33 | 0.00 | 0.33 | 0.47 | ▇▃▁▃▇ |
| effsize_u3 | 0 | 1.00 | 0.50 | 0.21 | 0.21 | 0.31 | 0.50 | 0.69 | 0.79 | ▇▃▁▃▇ |
| effsize_ov | 0 | 1.00 | 0.80 | 0.09 | 0.69 | 0.69 | 0.80 | 0.92 | 0.92 | ▇▁▇▁▇ |
| diff_cl | 480 | 0.50 | 0.01 | 0.18 | -0.54 | -0.06 | 0.00 | 0.07 | 0.64 | ▁▂▇▂▁ |
| diff_u3 | 480 | 0.50 | -0.24 | 0.29 | -0.79 | -0.49 | -0.21 | -0.03 | 0.69 | ▅▆▇▂▁ |
| diff_ov | 480 | 0.50 | -0.09 | 0.29 | -0.92 | -0.12 | 0.01 | 0.10 | 0.31 | ▂▁▁▇▅ |
| diffi_normed | 0 | 1.00 | 0.02 | 0.57 | -1.00 | -0.33 | 0.00 | 0.33 | 1.00 | ▇▆▆▆▇ |
| infor_normed | 0 | 1.00 | 0.04 | 0.48 | -1.00 | -0.33 | 0.00 | 0.33 | 1.00 | ▅▅▇▇▅ |
| value_normed | 0 | 1.00 | 0.04 | 0.50 | -1.00 | -0.33 | 0.00 | 0.33 | 1.00 | ▅▃▆▇▅ |
| rating_u3_filtered | 744 | 0.22 | 43.06 | 17.90 | 0.00 | 30.00 | 45.00 | 55.00 | 80.00 | ▂▃▇▇▂ |
| rating_ov_filtered | 588 | 0.39 | 82.60 | 16.81 | 0.00 | 80.00 | 85.00 | 90.00 | 100.00 | ▁▁▁▅▇ |
| diff_u3_filtered | 744 | 0.22 | -0.06 | 0.22 | -0.79 | -0.15 | -0.03 | 0.06 | 0.54 | ▁▂▇▅▁ |
| diff_ov_filtered | 588 | 0.39 | 0.02 | 0.17 | -0.92 | -0.02 | 0.05 | 0.10 | 0.31 | ▁▁▁▇▅ |
| sensi_binary | 480 | 0.50 | 0.70 | 0.46 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▃▁▁▁▇ |
| sensi_binary_filtered | 507 | 0.47 | 0.68 | 0.47 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▃▁▁▁▇ |
| effsize_abs | 0 | 1.00 | 0.50 | 0.25 | 0.20 | 0.20 | 0.50 | 0.80 | 0.80 | ▇▁▇▁▇ |
# create a list of u3_misconceptualizers
u3_misconceptualizers <-
study1_w %>%
filter(rating_u3_missconcept == T) %>%
pull(session) %>%
unique()
# create a list of ov_misconceptualizers
ov_misconceptualizers <-
study1_w %>%
filter(rating_ov_missconcept == T) %>%
pull(session) %>%
unique()
### wrangle time stamp data ####################################################
study1_w_timestamp <-
read_csv(here("data/teachers_study1_N40_detailed.csv")) %>%
# filter participants from study1_w only
filter(session %in% study1_w$session) %>%
# we only need vars sensitivity or accuracy
dplyr::filter(str_detect(item_name, "sensi|acccl|accu3|accov")) %>%
# create var with plot number
mutate(plot = paste0("plotx_", str_sub(item_name, -2, -1)),
# recode wrong item labelling
plot = ifelse(plot == "plotx__6", "plotx_06", plot)) %>%
relocate(session, plot) %>%
# delete the page number in item name
mutate(item_name = str_sub(item_name, 1, 5)) %>%
pivot_wider(id_cols = c(session, plot), names_from = item_name,
values_from = answered_relative) %>%
rowwise() %>%
# what was the time of the first item to be clicked?
mutate(effic = min(sensi, acccl, accu3, accov, na.rm=T)) %>%
ungroup() %>%
dplyr::select(session, plot, effic, sensi, acccl, accu3, accov) %>%
left_join(., study1_w %>%
select(session, plot, type), by=c("session", "plot")) %>%
# generate data set so that the six plots from the same type are ordered
# one after the other (and not 1-24)
group_by(session, type) %>%
arrange(plot) %>%
mutate(plotNrWithin = 1:n()) %>%
ungroup() %>%
group_by(plotNrWithin, type) %>%
mutate(effic_10righttrunc = ifelse(effic > quantile(effic, .9), NA, effic),
effic_05righttrunc = ifelse(effic > quantile(effic, .95), NA, effic),
log_effic_05righttrunc =log(effic_05righttrunc),
log_effic_10righttrunc = log(effic_10righttrunc),
plotNrWithin0 = plotNrWithin -1,
plotNrWithin_factor = as.factor(plotNrWithin)) %>%
ungroup()
skim(study1_w_timestamp)| Name | study1_w_timestamp |
| Number of rows | 960 |
| Number of columns | 15 |
| _______________________ | |
| Column type frequency: | |
| character | 3 |
| factor | 1 |
| numeric | 11 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| session | 0 | 1 | 64 | 64 | 0 | 40 | 0 |
| plot | 0 | 1 | 8 | 8 | 0 | 24 | 0 |
| type | 0 | 1 | 13 | 19 | 0 | 4 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| plotNrWithin_factor | 0 | 1 | FALSE | 6 | 1: 160, 2: 160, 3: 160, 4: 160 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| effic | 0 | 1.00 | 28817.81 | 67778.16 | 2419.00 | 9065.93 | 14866.60 | 26444.00 | 1342595.60 | ▇▁▁▁▁ |
| sensi | 480 | 0.50 | 22664.69 | 53591.36 | 2419.00 | 7670.72 | 11408.65 | 20709.33 | 684967.00 | ▇▁▁▁▁ |
| acccl | 557 | 0.42 | 33590.02 | 83104.51 | 3547.30 | 11650.00 | 18116.60 | 32103.65 | 1349401.70 | ▇▁▁▁▁ |
| accu3 | 480 | 0.50 | 61388.83 | 88359.00 | 5599.30 | 22174.12 | 39723.25 | 70041.55 | 1342595.60 | ▇▁▁▁▁ |
| accov | 480 | 0.50 | 69364.58 | 90056.87 | 6901.30 | 26939.18 | 46882.25 | 79074.25 | 1344927.30 | ▇▁▁▁▁ |
| plotNrWithin | 0 | 1.00 | 3.50 | 1.71 | 1.00 | 2.00 | 3.50 | 5.00 | 6.00 | ▇▃▃▃▃ |
| effic_10righttrunc | 96 | 0.90 | 16914.40 | 11789.22 | 2419.00 | 8734.20 | 13402.60 | 21572.35 | 89854.90 | ▇▂▁▁▁ |
| effic_05righttrunc | 48 | 0.95 | 19435.38 | 17815.29 | 2419.00 | 8910.87 | 14118.35 | 23782.15 | 168247.90 | ▇▁▁▁▁ |
| log_effic_05righttrunc | 48 | 0.95 | 9.60 | 0.71 | 7.79 | 9.10 | 9.56 | 10.08 | 12.03 | ▂▇▇▃▁ |
| log_effic_10righttrunc | 96 | 0.90 | 9.53 | 0.64 | 7.79 | 9.08 | 9.50 | 9.98 | 11.41 | ▁▆▇▅▁ |
| plotNrWithin0 | 0 | 1.00 | 2.50 | 1.71 | 0.00 | 1.00 | 2.50 | 4.00 | 5.00 | ▇▃▃▃▃ |
# socio demographics
sociodemographics <- read_delim("data/teachers_study1a.csv", delim = ";") %>%
select(session, mcstu, texpe, mcsub) %>%
# filter(!is.na(mcstu & texpe)) %>%
mutate(reply = session %in% c(study1_w$session)) %>%
filter(!reply == "FALSE") %>%
select(-reply) %>%
mutate(mcstu = as.factor(mcstu),
texpe = as.numeric(texpe),
mcsub = as.factor(mcsub),
subject_stem = grepl("1", mcsub),
subject_languages = grepl("2", mcsub),
subject_humanities_socialscience = grepl("3", mcsub),
subject_asthetic = grepl("4", mcsub))
skim(sociodemographics) | Name | sociodemographics |
| Number of rows | 40 |
| Number of columns | 8 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| factor | 2 |
| logical | 4 |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| session | 0 | 1 | 64 | 64 | 0 | 40 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| mcstu | 0 | 1 | FALSE | 6 | 6: 15, 1: 9, 2: 7, 3: 6 |
| mcsub | 0 | 1 | FALSE | 10 | 1: 8, 3: 8, 2: 7, 1, : 3 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| subject_stem | 0 | 1 | 0.48 | FAL: 21, TRU: 19 |
| subject_languages | 0 | 1 | 0.45 | FAL: 22, TRU: 18 |
| subject_humanities_socialscience | 0 | 1 | 0.50 | FAL: 20, TRU: 20 |
| subject_asthetic | 0 | 1 | 0.15 | FAL: 34, TRU: 6 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| texpe | 0 | 1 | 10.15 | 8.66 | 2 | 4 | 7 | 13.5 | 37.5 | ▇▃▂▁▁ |
study1_w_demo <- full_join(study1_w, sociodemographics, by = "session") %>%
mutate(schooltype_binary = ifelse(mcstu == 6, 1, 0)) # 6 = otherN = 15 participants checked “other” when asked which school type they teach at. This might be problematic as we cannot guarantee that these participants are actual school teachers. Hence, we decided to explore differences between those participants who indicated to work at a a specific school type and those that indicated “other” (see section Are there Differences Between Particpants That Indicated a Specific School Type and Those That Indicated “other”?).
skim(study1_w %>%
select(diffi,infor,value,rating_u3, rating_ov, rating_cl))| Name | %>%(…) |
| Number of rows | 960 |
| Number of columns | 6 |
| _______________________ | |
| Column type frequency: | |
| numeric | 6 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| diffi | 0 | 1.0 | 4.05 | 1.71 | 1 | 3.0 | 4 | 5.0 | 7 | ▇▆▆▆▇ |
| infor | 0 | 1.0 | 4.11 | 1.45 | 1 | 3.0 | 4 | 5.0 | 7 | ▅▅▇▇▅ |
| value | 0 | 1.0 | 4.13 | 1.49 | 1 | 3.0 | 4 | 5.0 | 7 | ▅▃▆▇▅ |
| rating_u3 | 480 | 0.5 | 26.17 | 22.60 | 0 | 6.0 | 18 | 45.0 | 100 | ▇▃▃▁▁ |
| rating_ov | 480 | 0.5 | 71.63 | 28.63 | 0 | 65.0 | 80 | 90.0 | 100 | ▂▁▁▅▇ |
| rating_cl | 480 | 0.5 | 0.01 | 0.38 | -1 | -0.2 | 0 | 0.2 | 1 | ▂▆▇▃▁ |
skim(study1_w %>%
select(diffi, infor,value,rating_u3, rating_ov, rating_cl, session, type, plot) %>%
gather(var, value, diffi, infor,value,rating_u3, rating_ov, rating_cl)%>%
mutate(variable = paste(var, type, sep = "_"))%>%
select(-type, -var)%>%
group_by(session, plot) %>%
spread(variable, value) %>%
ungroup())| Name | %>%(…) |
| Number of rows | 960 |
| Number of columns | 26 |
| _______________________ | |
| Column type frequency: | |
| character | 2 |
| numeric | 24 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| session | 0 | 1 | 64 | 64 | 0 | 40 | 0 |
| plot | 0 | 1 | 8 | 8 | 0 | 24 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| diffi_gardneraltman_xaxis | 720 | 0.25 | 3.59 | 1.87 | 1 | 2.00 | 3.0 | 5.00 | 7.0 | ▇▅▃▃▅ |
| diffi_halfeye_xaxis | 720 | 0.25 | 4.23 | 1.54 | 1 | 3.00 | 4.0 | 6.00 | 7.0 | ▅▇▆▆▇ |
| diffi_halfeye_yaxis | 720 | 0.25 | 4.55 | 1.58 | 1 | 3.00 | 5.0 | 6.00 | 7.0 | ▃▃▃▆▇ |
| diffi_raincloud_yaxis | 720 | 0.25 | 3.83 | 1.68 | 1 | 2.75 | 4.0 | 5.00 | 7.0 | ▇▆▆▅▆ |
| infor_gardneraltman_xaxis | 720 | 0.25 | 3.80 | 1.57 | 1 | 3.00 | 4.0 | 5.00 | 7.0 | ▆▅▆▇▃ |
| infor_halfeye_xaxis | 720 | 0.25 | 4.19 | 1.34 | 1 | 3.00 | 4.0 | 5.00 | 7.0 | ▃▆▇▇▅ |
| infor_halfeye_yaxis | 720 | 0.25 | 4.40 | 1.35 | 1 | 3.00 | 4.5 | 5.00 | 7.0 | ▂▅▇▇▆ |
| infor_raincloud_yaxis | 720 | 0.25 | 4.04 | 1.48 | 1 | 3.00 | 4.0 | 5.00 | 7.0 | ▅▃▇▆▅ |
| rating_cl_gardneraltman_xaxis | 843 | 0.12 | 0.03 | 0.40 | -1 | -0.20 | 0.0 | 0.20 | 1.0 | ▂▅▇▃▁ |
| rating_cl_halfeye_xaxis | 834 | 0.13 | 0.00 | 0.36 | -1 | -0.20 | 0.0 | 0.20 | 1.0 | ▁▇▇▂▁ |
| rating_cl_halfeye_yaxis | 842 | 0.12 | 0.01 | 0.40 | -1 | -0.20 | 0.0 | 0.20 | 1.0 | ▂▇▇▃▁ |
| rating_cl_raincloud_yaxis | 841 | 0.12 | 0.03 | 0.38 | -1 | -0.20 | 0.0 | 0.20 | 0.8 | ▁▃▇▆▂ |
| rating_ov_gardneraltman_xaxis | 843 | 0.12 | 68.63 | 30.39 | 0 | 60.00 | 80.0 | 90.00 | 100.0 | ▂▁▂▅▇ |
| rating_ov_halfeye_xaxis | 834 | 0.13 | 74.73 | 26.46 | 0 | 70.00 | 85.0 | 90.00 | 100.0 | ▂▁▁▃▇ |
| rating_ov_halfeye_yaxis | 842 | 0.12 | 70.08 | 30.92 | 0 | 66.25 | 80.0 | 90.00 | 100.0 | ▂▁▁▅▇ |
| rating_ov_raincloud_yaxis | 841 | 0.12 | 72.83 | 26.57 | 0 | 67.50 | 80.0 | 90.00 | 100.0 | ▂▁▁▅▇ |
| rating_u3_gardneraltman_xaxis | 843 | 0.12 | 26.56 | 21.61 | 0 | 7.00 | 22.0 | 45.00 | 100.0 | ▇▃▅▁▁ |
| rating_u3_halfeye_xaxis | 834 | 0.13 | 26.73 | 23.01 | 0 | 5.25 | 20.0 | 48.75 | 80.0 | ▇▂▂▃▁ |
| rating_u3_halfeye_yaxis | 842 | 0.12 | 27.08 | 23.33 | 0 | 7.00 | 15.0 | 50.00 | 75.0 | ▇▁▂▃▁ |
| rating_u3_raincloud_yaxis | 841 | 0.12 | 24.28 | 22.54 | 0 | 5.50 | 15.0 | 40.00 | 100.0 | ▇▃▂▁▁ |
| value_gardneraltman_xaxis | 720 | 0.25 | 3.80 | 1.51 | 1 | 3.00 | 4.0 | 5.00 | 7.0 | ▆▅▇▇▃ |
| value_halfeye_xaxis | 720 | 0.25 | 4.31 | 1.40 | 1 | 3.75 | 5.0 | 5.00 | 7.0 | ▃▃▆▇▅ |
| value_halfeye_yaxis | 720 | 0.25 | 4.38 | 1.39 | 1 | 4.00 | 5.0 | 5.00 | 7.0 | ▂▂▅▇▃ |
| value_raincloud_yaxis | 720 | 0.25 | 4.03 | 1.58 | 1 | 3.00 | 4.0 | 5.00 | 7.0 | ▆▃▆▇▅ |
skim(study1_w_timestamp %>%
select(effic, session))| Name | study1_w_timestamp %>% se… |
| Number of rows | 960 |
| Number of columns | 2 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| session | 0 | 1 | 64 | 64 | 0 | 40 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| effic | 0 | 1 | 28817.81 | 67778.16 | 2419 | 9065.93 | 14866.6 | 26444 | 1342596 | ▇▁▁▁▁ |
skim(study1_w_timestamp %>%
select(effic, session, type, plot) %>%
gather(var, value, effic)%>%
mutate(variable = paste(var, type, sep = "_"))%>%
select(-type, -var)%>%
group_by(session, plot) %>%
spread(variable, value) %>%
ungroup())| Name | %>%(…) |
| Number of rows | 960 |
| Number of columns | 6 |
| _______________________ | |
| Column type frequency: | |
| character | 2 |
| numeric | 4 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| session | 0 | 1 | 64 | 64 | 0 | 40 | 0 |
| plot | 0 | 1 | 8 | 8 | 0 | 24 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| effic_gardneraltman_xaxis | 720 | 0.25 | 35739.25 | 99037.01 | 3547.3 | 10743.50 | 17592.75 | 32584.00 | 1342595.6 | ▇▁▁▁▁ |
| effic_halfeye_xaxis | 720 | 0.25 | 27179.72 | 62047.66 | 2897.0 | 8788.35 | 13740.70 | 23012.73 | 684967.0 | ▇▁▁▁▁ |
| effic_halfeye_yaxis | 720 | 0.25 | 23954.41 | 44387.89 | 2419.0 | 8960.30 | 13016.15 | 23628.10 | 466834.3 | ▇▁▁▁▁ |
| effic_raincloud_yaxis | 720 | 0.25 | 28397.86 | 52248.67 | 2769.0 | 8910.65 | 14126.85 | 25963.05 | 598871.6 | ▇▁▁▁▁ |
study1_w_demo %>%
ggplot(., aes(effsize, rating_cl, color = as.factor(schooltype_binary))) +
geom_jitter() +
stat_smooth() +
ylab("Abstract metric") +
xlab("Plotted effect size") +
labs(color = "Schooltype") +
theme_modern_rc()
study1_w_demo%>%
ggplot(., aes(effsize, rating_u3, color = as.factor(schooltype_binary))) +
geom_jitter() +
stat_smooth() +
ylab("Cohen's U3 metric") +
xlab("Plotted effect size") +
labs(color = "Schooltype") +
theme_modern_rc()
study1_w_demo %>%
ggplot(., aes(abs(effsize), rating_ov, color = as.factor(schooltype_binary))) +
geom_jitter() +
stat_smooth(method = "lm") +
ylab("Overlap metric") +
xlab("Plotted effect size") +
labs(color = "Schooltype") +
theme_modern_rc()
study1_w_demo %>%
ggplot(., aes(as.factor(schooltype_binary), infor)) +
geom_jitter()+
stat_summary(fun.data = mean_sdl,
fun.args = list(mult =1),
color = "white") +
ylab("Perceived Informativity") +
xlab("School Type 1") +
theme_modern_rc()
study1_w_demo %>%
ggplot(., aes(as.factor(schooltype_binary), diffi)) +
geom_jitter()+
stat_summary(fun.data = mean_sdl,
fun.args = list(mult =1),
color = "white") +
ylab("Perceived Difficulty") +
xlab("School Type") +
theme_modern_rc()
study1_w_demo %>%
ggplot(., aes(as.factor(schooltype_binary), value)) +
geom_jitter()+
stat_summary(fun.data = mean_sdl,
fun.args = list(mult =1),
color = "white") +
ylab("Perceived Value") +
xlab("School Type 1=others") +
theme_modern_rc()
rating_cl, rating_u3 and rating_ov## accuracy U3
### overall
study1_w_demo %>%
do(tau_u3 = unlist(cor(.$effsize, .$rating_u3, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_u3)# A tibble: 1 × 1
tau_u3
<dbl>
1 -0.0837### grouped by plot type
study1_w_demo %>%
group_by(type) %>%
do(tau_u3 = unlist(cor(.$effsize, .$rating_u3, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_u3)# A tibble: 4 × 2
type tau_u3
<chr> <dbl>
1 gardneraltman_xaxis -0.0334
2 halfeye_xaxis -0.143
3 halfeye_yaxis -0.0423
4 raincloud_yaxis -0.118 ### grouped by school type
study1_w_demo %>%
group_by(schooltype_binary) %>%
do(tau_u3a = unlist(cor(.$effsize, .$rating_u3, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_u3a)# A tibble: 2 × 2
schooltype_binary tau_u3a
<dbl> <dbl>
1 0 -0.0649
2 1 -0.122 ### grouped by school type and plot type
study1_w_demo %>%
group_by(schooltype_binary, type) %>%
do(tau_u3 = unlist(cor(abs(.$effsize), .$rating_u3, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_u3)# A tibble: 8 × 3
schooltype_binary type tau_u3
<dbl> <chr> <dbl>
1 0 gardneraltman_xaxis 0.105
2 0 halfeye_xaxis 0.188
3 0 halfeye_yaxis 0.210
4 0 raincloud_yaxis 0.0956
5 1 gardneraltman_xaxis 0.0805
6 1 halfeye_xaxis 0.131
7 1 halfeye_yaxis 0.0252
8 1 raincloud_yaxis 0.133 ## overlap
### overall
study1_w_demo %>%
do(tau_ov = unlist(cor(abs(.$effsize), .$rating_ov, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_ov)# A tibble: 1 × 1
tau_ov
<dbl>
1 -0.276### grouped by plot type
study1_w_demo %>%
group_by(type) %>%
do(tau_ov = unlist(cor(abs(.$effsize), .$rating_ov, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_ov)# A tibble: 4 × 2
type tau_ov
<chr> <dbl>
1 gardneraltman_xaxis -0.188
2 halfeye_xaxis -0.228
3 halfeye_yaxis -0.326
4 raincloud_yaxis -0.348### grouped by school type
study1_w_demo %>%
group_by(schooltype_binary) %>%
do(tau_ov = unlist(cor(abs(.$effsize), .$rating_ov, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_ov)# A tibble: 2 × 2
schooltype_binary tau_ov
<dbl> <dbl>
1 0 -0.233
2 1 -0.345### grouped by school type and plot type
study1_w_demo %>%
group_by(schooltype_binary, type) %>%
do(tau_ov = unlist(cor(abs(.$effsize), .$rating_ov, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_ov)# A tibble: 8 × 3
schooltype_binary type tau_ov
<dbl> <chr> <dbl>
1 0 gardneraltman_xaxis -0.201
2 0 halfeye_xaxis -0.105
3 0 halfeye_yaxis -0.195
4 0 raincloud_yaxis -0.410
5 1 gardneraltman_xaxis -0.155
6 1 halfeye_xaxis -0.434
7 1 halfeye_yaxis -0.541
8 1 raincloud_yaxis -0.248## Abstract Metric
### overall
study1_w_demo %>%
do(tau_cl = unlist(cor(.$effsize, .$rating_cl, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_cl)# A tibble: 1 × 1
tau_cl
<dbl>
1 0.449### grouped by plot type
study1_w_demo %>%
group_by(type) %>%
do(tau_cl = unlist(cor(.$effsize, .$rating_cl, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_cl)# A tibble: 4 × 2
type tau_cl
<chr> <dbl>
1 gardneraltman_xaxis 0.373
2 halfeye_xaxis 0.548
3 halfeye_yaxis 0.484
4 raincloud_yaxis 0.415### grouped by school type
study1_w_demo %>%
group_by(schooltype_binary) %>%
do(tau_cl = unlist(cor(.$effsize, .$rating_cl, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_cl)# A tibble: 2 × 2
schooltype_binary tau_cl
<dbl> <dbl>
1 0 0.373
2 1 0.592### grouped by school type and plot type
study1_w_demo %>%
group_by(schooltype_binary, type) %>%
do(tau_cl = unlist(cor(.$effsize, .$rating_cl, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_cl)# A tibble: 8 × 3
schooltype_binary type tau_cl
<dbl> <chr> <dbl>
1 0 gardneraltman_xaxis 0.379
2 0 halfeye_xaxis 0.442
3 0 halfeye_yaxis 0.421
4 0 raincloud_yaxis 0.280
5 1 gardneraltman_xaxis 0.371
6 1 halfeye_xaxis 0.699
7 1 halfeye_yaxis 0.590
8 1 raincloud_yaxis 0.701study1_w_demo_vda <- study1_w_demo %>%
select(session, plot, rating_ov, rating_cl, rating_u3, schooltype_binary) %>%
pivot_longer(rating_ov:rating_u3, names_to = "variables",
values_to = "values") %>%
mutate(var = paste(variables, schooltype_binary, sep = "_")) %>%
select(-variables, values) %>%
group_by(session, plot) %>%
pivot_wider(names_from = "var", values_from = "values")The results do not substantially differ between the two groups. Hence, we will include the participants indicated “other” school types in the following analyses.
study1_w %>%
select(rating_cl, rating_u3, rating_ov) %>%
pivot_longer(
c(rating_cl, rating_u3, rating_ov),
names_to = "dependent_variable",
values_to = "rated_effectsize"
) %>%
ggplot(., aes(rated_effectsize)) +
geom_histogram() +
xlab("Rated effect size") +
facet_wrap(~dependent_variable, scales = "free_x",
labeller = as_labeller(c("rating_cl" = "Abstract metric",
"rating_ov" = "Overlap metric",
"rating_u3" = "Cohen's U3 metric"))) +
theme_modern_rc() +
theme(strip.text = element_text(color = "white"))
Somewhat disturbing is the first mode in rating_ov. Maybe some users confused overlap and non-overlap? Another artifact seems to be the first mode in rating_u3.
ggplot(study1_w %>%
select(rating_cl, rating_u3, rating_ov, effsize, effsize_cl, session) %>%
pivot_longer(
c(rating_cl, rating_u3, rating_ov),
names_to = "operationalization",
values_to = "judged_effectsize"
),
aes(judged_effectsize, as.numeric(as.factor(session)),
color = session)
) +
geom_jitter(height = 0) +
xlab("Rated effect size") +
ylab("Participant ID") +
facet_wrap(~ operationalization, scales = "free_x",
labeller = as_labeller(c("rating_cl" = "Abstract metric",
"rating_ov" = "Overlap metric",
"rating_u3" = "Cohen's U3 metric"))) +
theme_modern_rc() +
theme(legend.position = "none",
strip.text = element_text(color = "white"))
study1_w %>%
ggplot(., aes(effsize, rating_cl)) +
geom_jitter() +
stat_smooth() +
ylab("Abstract metric") +
xlab("Plotted effect size") +
theme_modern_rc()
study1_w %>%
ggplot(., aes(effsize, rating_u3, color = rating_u3_missconcept)) +
geom_jitter() +
stat_smooth() +
ylab("Cohen's U3 metric") +
xlab("Plotted effect size") +
labs(color = "Misconcept") +
theme_modern_rc()
study1_w %>%
ggplot(., aes(abs(effsize), rating_ov, color = rating_ov_missconcept)) +
geom_jitter() +
stat_smooth(method = "lm") +
ylab("Overlap metric") +
xlab("Plotted effect size") +
labs(color = "Misconcept") +
theme_modern_rc()
The correlations underpin the interpretation of the misconceptions. Therefore, we will look at the intercorrelations of the dependent variables.
rating_cl, rating_u3 and rating_ovstudy1_w %>%
group_by(type) %>%
do(tau_cl = unlist(cor(.$effsize, .$rating_cl, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_cl)# A tibble: 4 × 2
type tau_cl
<chr> <dbl>
1 gardneraltman_xaxis 0.373
2 halfeye_xaxis 0.548
3 halfeye_yaxis 0.484
4 raincloud_yaxis 0.415study1_w %>%
group_by(rating_ov_missconcept, type) %>%
do(tau_ov = unlist(cor(abs(.$effsize), .$rating_ov, method = "kendall",
use = "pairwise.complete"))) %>%
unnest(tau_ov)# A tibble: 8 × 3
rating_ov_missconcept type tau_ov
<lgl> <chr> <dbl>
1 FALSE gardneraltman_xaxis -0.355
2 FALSE halfeye_xaxis -0.384
3 FALSE halfeye_yaxis -0.478
4 FALSE raincloud_yaxis -0.523
5 TRUE gardneraltman_xaxis 0.0236
6 TRUE halfeye_xaxis 0.119
7 TRUE halfeye_yaxis 0.0205
8 TRUE raincloud_yaxis -0.220 study1_w %>%
group_by(rating_u3_missconcept, type) %>%
do(tau_u3 = cor(.$effsize, .$rating_u3, method = "kendall",
use = "pairwise.complete")) %>%
unnest(tau_u3)# A tibble: 8 × 3
rating_u3_missconcept type tau_u3
<lgl> <chr> <dbl>
1 FALSE gardneraltman_xaxis -0.298
2 FALSE halfeye_xaxis -0.471
3 FALSE halfeye_yaxis -0.263
4 FALSE raincloud_yaxis -0.321
5 TRUE gardneraltman_xaxis 0.119
6 TRUE halfeye_xaxis -0.153
7 TRUE halfeye_yaxis -0.108
8 TRUE raincloud_yaxis -0.0971ggplot(study1_w,
aes(rating_cl, rating_u3)) +
geom_jitter() +
xlab("Abstract metric") +
ylab("Cohen's U3 metric") +
theme_modern_rc() +
ggplot(study1_w,
aes(rating_cl, rating_ov)) +
geom_jitter() +
xlab("Abstract metric") +
ylab("Overlap metric") +
theme_modern_rc() +
ggplot(study1_w,
aes(rating_ov, rating_u3)) +
geom_jitter() +
xlab("Overlap metric") +
ylab("Cohen's U3 metric") +
theme_modern_rc()
study1_w %>%
select(diff_cl, diff_u3_filtered, diff_ov_filtered, session, type) %>%
gather(dependent_variable, difference_to_true_effsize,
diff_cl, diff_u3_filtered, diff_ov_filtered) %>%
ggplot(., aes(type, difference_to_true_effsize)) +
geom_boxplot(alpha = .3) +
facet_wrap(~dependent_variable, scales = "free_y",
labeller = as_labeller(c("diff_cl" = "Abstract metric",
"diff_ov_filtered" = "Overlap metric (filtered)",
"diff_u3_filtered" = "Cohen's U3 metric (filtered)"))) +
geom_jitter(aes(color = type)) +
ylab("Difference to true effect size") +
xlab("Accuracy item") +
labs(color = "Accuracy item") +
theme_modern_rc() +
theme(strip.text = element_text(color = "white"),
axis.text.x = element_blank())
#| results: hide
# Rating abstract metric
mod0_rating_cl <- brm(rating_cl ~ + (1|session),
data = study1_w,
iter = 20000,
save_pars = save_pars(all = TRUE),
cores = 4)
mod1_rating_cl <- brm(rating_cl ~ effsize + (1|session),
data = study1_w,
iter = 20000,
save_pars = save_pars(all = TRUE),
cores = 4)
mod2_rating_cl <- brm(rating_cl ~ type + effsize + (1|session),
data = study1_w,
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)bayes_factor(mod1_rating_cl, mod0_rating_cl)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5Estimated Bayes factor in favor of mod1_rating_cl over mod0_rating_cl: 679684121064385479988489175957504.00000bayes_factor(mod2_rating_cl, mod1_rating_cl)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5Estimated Bayes factor in favor of mod2_rating_cl over mod1_rating_cl: 0.00117#| results: hide
# Rating Cohen's U3
mod0_rating_u3 <- brm(rating_u3 ~ + (1|session),
data = study1_w%>%
filter(rating_u3_missconcept == F),
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)
mod1_rating_u3 <- brm(rating_u3 ~ effsize + (1|session),
data = study1_w%>%
filter(rating_u3_missconcept == F),
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)
mod2_rating_u3 <- brm(rating_u3 ~ type + effsize + (1|session),
data = study1_w %>%
filter(rating_u3_missconcept == F),
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)bayes_factor(mod1_rating_u3, mod0_rating_u3)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4Estimated Bayes factor in favor of mod1_rating_u3 over mod0_rating_u3: 1063800646.61698bayes_factor(mod2_rating_u3, mod1_rating_u3)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5Estimated Bayes factor in favor of mod2_rating_u3 over mod1_rating_u3: 2372.26540#| results: hide
# Rating Overlap
mod0_rating_ov <- brm(rating_ov ~ + (1|session),
data = study1_w%>%
filter(rating_ov_missconcept == F),
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)
mod1_rating_ov <- brm(rating_ov ~ effsize + (1|session),
data = study1_w%>%
filter(rating_ov_missconcept == F),
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)
mod2_rating_ov <- brm(rating_ov ~ type + effsize + (1|session),
data = study1_w %>%
filter(rating_ov_missconcept == F),
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)bayes_factor(mod1_rating_ov, mod0_rating_ov)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4Estimated Bayes factor in favor of mod1_rating_ov over mod0_rating_ov: 4.78663bayes_factor(mod2_rating_ov, mod1_rating_ov)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 6
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 6Estimated Bayes factor in favor of mod2_rating_ov over mod1_rating_ov: 533.56624study1_w %>%
ggplot(aes(type, diffi)) +
geom_jitter() +
stat_summary(fun.data = mean_sdl,
fun.args = list(mult = 1),
color = "white") +
theme_modern_rc() +
labs(title = "Difficulty",
subtitle = "per Plot Type",
caption = "Means ± 1*SD")
# Perceived Difficulty
mod0_diffi <- brm(diffi ~ + (1|session),
data = study1_w,
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)
mod1_diffi <- brm(diffi ~ effsize + (1|session),
data = study1_w,
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)
mod2_diffi <- brm(diffi ~ type + effsize + (1|session),
data = study1_w,
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)bayes_factor(mod1_diffi, mod0_diffi)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5Estimated Bayes factor in favor of mod1_diffi over mod0_diffi: 0.40277bayes_factor(mod2_diffi, mod1_diffi)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5Estimated Bayes factor in favor of mod2_diffi over mod1_diffi: 4783910927757722.00000study1_w %>%
ggplot(aes(type, infor)) +
geom_jitter() +
stat_summary(fun.data = mean_sdl,
fun.args = list(mult = 1),
color = "white") +
theme_modern_rc() +
labs(title = "Informativity",
subtitle = "per Plot Type",
caption = "Means ± 1*SD")
# Perceived Informativity
mod0_infor <- brm(infor ~ + (1|session),
data = study1_w,
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)
mod1_infor <- brm(infor ~ effsize + (1|session),
data = study1_w,
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)
mod2_infor <- brm(infor ~ type + effsize + (1|session),
data = study1_w,
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)bayes_factor(mod1_infor, mod0_infor)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5Estimated Bayes factor in favor of mod1_infor over mod0_infor: 0.25977bayes_factor(mod2_infor, mod1_infor)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 6
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 6Estimated Bayes factor in favor of mod2_infor over mod1_infor: 736360.24376study1_w %>%
ggplot(aes(type, value)) +
geom_jitter() +
stat_summary(fun.data = mean_sdl,
fun.args = list(mult = 1),
color = "white") +
theme_modern_rc() +
labs(title = "Perceived Value",
subtitle = "per Plot Type",
caption = "Means ± 1*SD")
# Perceived Value
mod0_value <- brm(value ~ + (1|session),
data = study1_w,
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)
mod1_value <- brm(value ~ effsize + (1|session),
data = study1_w,
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)
mod2_value <- brm(value ~ type + effsize + (1|session),
data = study1_w,
iter = 10000,
save_pars = save_pars(all = TRUE),
cores = 4
)bayes_factor(mod1_value, mod0_value)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 6
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 6Estimated Bayes factor in favor of mod1_value over mod0_value: 0.21777bayes_factor(mod2_value, mod1_value)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5Estimated Bayes factor in favor of mod2_value over mod1_value: 84381611.39081sjPlot::tab_model(
mod0_rating_cl, mod0_rating_u3, mod0_rating_ov,
mod0_diffi, mod0_infor, mod0_value,
mod1_rating_cl, mod1_rating_u3, mod1_rating_ov,
mod1_diffi, mod1_infor, mod1_value,
mod2_rating_cl, mod2_rating_u3, mod2_rating_ov,
mod2_diffi, mod2_infor, mod2_value
)| rating_cl | rating_u3 | rating_ov | diffi | infor | value | rating_cl | rating_u3 | rating_ov | diffi | infor | value | rating_cl | rating_u3 | rating_ov | diffi | infor | value | |||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) |
| Intercept | 0.02 | -0.02 – 0.05 | 43.11 | 38.56 – 47.65 | 82.70 | 80.14 – 85.21 | 4.06 | 3.69 – 4.44 | 4.11 | 3.80 – 4.40 | 4.12 | 3.79 – 4.48 | 0.01 | -0.02 – 0.04 | 43.36 | 38.66 – 47.90 | 82.67 | 80.14 – 85.22 | 4.05 | 3.68 – 4.42 | 4.11 | 3.81 – 4.41 | 4.14 | 3.80 – 4.49 | 0.01 | -0.05 – 0.07 | 42.72 | 36.96 – 48.44 | 82.29 | 78.35 – 86.24 | 3.58 | 3.19 – 3.99 | 3.80 | 3.48 – 4.12 | 3.79 | 3.43 – 4.16 |
| effsize | 0.37 | 0.32 – 0.42 | -11.80 | -15.37 – -8.16 | -1.03 | -4.09 – 2.00 | -0.09 | -0.24 – 0.06 | -0.06 | -0.19 – 0.07 | -0.05 | -0.17 – 0.07 | 0.37 | 0.32 – 0.42 | -11.61 | -15.22 – -7.99 | -1.09 | -4.11 – 1.89 | -0.09 | -0.23 – 0.05 | -0.06 | -0.19 – 0.06 | -0.05 | -0.17 – 0.07 | ||||||||||||
| typehalfeye_xaxis | -0.02 | -0.10 – 0.06 | 0.54 | -5.05 – 6.11 | 2.49 | -2.20 – 7.20 | 0.65 | 0.42 – 0.87 | 0.39 | 0.19 – 0.59 | 0.51 | 0.32 – 0.70 | ||||||||||||||||||||||||
| typehalfeye_yaxis | 0.01 | -0.08 – 0.09 | 4.05 | -1.60 – 9.79 | -0.98 | -5.83 – 3.76 | 0.96 | 0.75 – 1.19 | 0.61 | 0.40 – 0.81 | 0.58 | 0.40 – 0.77 | ||||||||||||||||||||||||
| typeraincloud_yaxis | 0.01 | -0.08 – 0.09 | -1.91 | -7.50 – 3.72 | -0.06 | -4.94 – 4.65 | 0.24 | 0.02 – 0.46 | 0.24 | 0.04 – 0.45 | 0.23 | 0.04 – 0.42 | ||||||||||||||||||||||||
| Random Effects | ||||||||||||||||||||||||||||||||||||
| σ2 | 0.15 | 259.18 | 254.94 | 1.72 | 1.32 | 1.17 | 0.11 | 213.83 | 255.11 | 1.72 | 1.32 | 1.17 | 0.11 | 212.91 | 256.10 | 1.58 | 1.28 | 1.11 | ||||||||||||||||||
| τ00 | 0.00 session | 75.01 session | 30.15 session | 1.31 session | 0.86 session | 1.15 session | 0.00 session | 82.48 session | 29.64 session | 1.31 session | 0.86 session | 1.16 session | 0.00 session | 79.40 session | 29.09 session | 1.32 session | 0.87 session | 1.15 session | ||||||||||||||||||
| ICC | 0.01 | 0.22 | 0.11 | 0.43 | 0.40 | 0.50 | 0.02 | 0.28 | 0.10 | 0.43 | 0.40 | 0.50 | 0.02 | 0.27 | 0.10 | 0.46 | 0.41 | 0.51 | ||||||||||||||||||
| N | 40 session | 18 session | 31 session | 40 session | 40 session | 40 session | 40 session | 18 session | 31 session | 40 session | 40 session | 40 session | 40 session | 18 session | 31 session | 40 session | 40 session | 40 session | ||||||||||||||||||
| Observations | 480 | 216 | 372 | 960 | 960 | 960 | 480 | 216 | 372 | 960 | 960 | 960 | 480 | 216 | 372 | 960 | 960 | 960 | ||||||||||||||||||
| Marginal R2 / Conditional R2 | 0.000 / 0.006 | 0.000 / 0.198 | 0.000 / 0.099 | 0.000 / 0.414 | 0.000 / 0.377 | 0.000 / 0.476 | 0.279 / 0.291 | 0.134 / 0.344 | 0.002 / 0.101 | 0.001 / 0.414 | 0.001 / 0.377 | 0.000 / 0.476 | 0.282 / 0.294 | 0.155 / 0.358 | 0.015 / 0.112 | 0.049 / 0.463 | 0.025 / 0.401 | 0.026 / 0.501 | ||||||||||||||||||
ggplot(study1_w %>%
select(sensi_ordinal, type) %>%
na.omit(), aes(sensi_ordinal,
color = sensi_ordinal,
fill = sensi_ordinal)) +
facet_wrap(~ type) +
geom_bar() +
theme_modern_rc() +
theme(strip.text = element_text(color = "white")) +
ggtitle("Sensitivity", "all participants")
ggplot(study1_w %>%
select(sensi_ordinal, type, session) %>%
na.omit() %>%
filter(!session %in% ov_misconceptualizers &
!session %in% u3_misconceptualizers), aes(sensi_ordinal,
color = sensi_ordinal,
fill = sensi_ordinal)) +
facet_wrap(~ type) +
geom_bar() +
theme_modern_rc() +
theme(strip.text = element_text(color = "white")) +
ggtitle("Sensitivity", "without missconceptualizers")
ggplot(study1_w %>%
select(sensi_correct, type, session) %>%
dplyr::filter(!is.na(sensi_correct)),
aes(sensi_correct,
color = sensi_correct,
fill = sensi_correct)) +
facet_wrap(~ type) +
geom_bar() +
theme_modern_rc() +
theme(strip.text = element_text(color = "white"),
axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(color = "", fill = "") +
ggtitle("Binary Sensitivity", "without wrong decisions")
ggplot(study1_w %>%
select(sensi_binary_filtered, type, session),
aes(sensi_binary_filtered,
color = as.factor(sensi_binary_filtered),
fill = as.factor(sensi_binary_filtered))) +
facet_wrap(~ type) +
geom_bar() +
theme_modern_rc() +
theme(strip.text = element_text(color = "white")) +
labs(color = "", fill = "") +
ggtitle("Binary Sensitivity", "without wrong decisions (0=equal, 1=right direction)")
Fitting a series of logistic regressions with non-informative priors
#| results: hide
logreg_mod0 <- brm(
sensi_binary_filtered ~ +(1 | session),
family = bernoulli(link = "logit"),
data = study1_w,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4
)
logreg_mod1 <-
brm(
sensi_binary_filtered ~ effsize_abs + (1 | session),
family = bernoulli(link = "logit"),
data = study1_w,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4
)
logreg_mod2 <-
brm(
sensi_binary_filtered ~ effsize_abs + type + (1 | session),
family = bernoulli(link = "logit"),
data = study1_w,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4
)bayes_factor(logreg_mod1, logreg_mod0)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 6
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 6
Iteration: 7Estimated Bayes factor in favor of logreg_mod1 over logreg_mod0: 424647358621071558465011318784.00000bayes_factor(logreg_mod2, logreg_mod1)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 6
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 6
Iteration: 7
Iteration: 8Estimated Bayes factor in favor of logreg_mod2 over logreg_mod1: 16.17254sjPlot::tab_model(logreg_mod0, logreg_mod1, logreg_mod2, show.icc = TRUE)| sensi_binary_filtered | sensi_binary_filtered | sensi_binary_filtered | ||||
|---|---|---|---|---|---|---|
| Predictors | Odds Ratios | CI (95%) | Odds Ratios | CI (95%) | Odds Ratios | CI (95%) |
| Intercept | 3.10 | 1.60 – 6.32 | 0.11 | 0.03 – 0.38 | 0.08 | 0.02 – 0.32 |
| effsize_abs | 2851.82 | 482.39 – 22551.19 | 3354.91 | 601.26 – 24919.72 | ||
| typehalfeye_xaxis | 1.86 | 0.71 – 5.06 | ||||
| typehalfeye_yaxis | 1.79 | 0.71 – 4.64 | ||||
| typeraincloud_yaxis | 0.75 | 0.30 – 1.86 | ||||
| Random Effects | ||||||
| σ2 | 3.29 | 3.29 | 3.29 | |||
| τ00 | 3.91 session | 10.11 session | 11.05 session | |||
| ICC | 0.54 | 0.75 | 0.77 | |||
| N | 40 session | 40 session | 40 session | |||
| Observations | 453 | 453 | 453 | |||
| Marginal R2 / Conditional R2 | 0.000 / 0.317 | 0.300 / 0.598 | 0.312 / 0.604 | |||
pp_check(logreg_mod2) +
theme_modern_rc() +
scale_color_manual(values=viridis(2, begin = .3))
ggplot(study1_w_timestamp, aes(as.factor(plotNrWithin), effic)) +
geom_boxplot(alpha = .2, color = "lightgrey") +
geom_sina(alpha = .5) +
coord_cartesian(ylim = c(0,100000)) +
facet_wrap(~type) +
theme_modern_rc() +
labs(title ="Dwell Times Until First Decision",
subtitle = "Per Plot Type and Plot Repetition") +
theme(strip.text = element_text(color = "white"))
ggplot(study1_w_timestamp, aes(as.factor(plotNrWithin), effic_05righttrunc)) +
geom_boxplot(alpha = .2, color = "lightgrey") +
geom_sina(alpha = .5) +
coord_cartesian(ylim = c(0,85000)) +
facet_wrap(~type) +
theme_modern_rc() +
labs(title ="5% Truncated Dwell Times Until First Decision",
subtitle = "Per Plot Type and Plot Repetition") +
theme(strip.text = element_text(color = "white"))
ggplot(study1_w_timestamp, aes(as.factor(plotNrWithin), effic_10righttrunc)) +
geom_boxplot(alpha = .2, color = "lightgrey") +
geom_sina(alpha = .5) +
coord_cartesian(ylim = c(0,85000)) +
facet_wrap(~type) +
theme_modern_rc() +
labs(title ="10% Truncated Dwell Times Until First Decision",
subtitle = "Per Plot Type and Plot Repetition") +
theme(strip.text = element_text(color = "white"))
log() Transformedggplot(study1_w_timestamp,
aes(as.factor(plotNrWithin),
log(effic_05righttrunc))) +
geom_boxplot(alpha = .2, color = "lightgrey") +
geom_sina(alpha = .5) +
facet_wrap(~type) +
theme_modern_rc() +
labs(title ="log Transformed Dwell Times Until First Decision",
subtitle = "Per Plot Type and Plot Repetition") +
theme(strip.text = element_text(color = "white"))
{brms}#| error: false
#| warning: false
#| results: hide
study1_w_timestamp_effsize <- study1_w_timestamp %>%
full_join(study1_w %>% select(session, effsize), by = "session")
plot01_mod0 <-
brm(log_effic_05righttrunc ~ 1 + (1 | session),
data = study1_w_timestamp_effsize %>%
filter(plotNrWithin == 1),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 6000)
pp_check(plot01_mod0) +
theme_modern_rc() +
scale_color_manual(values=viridis(2, begin = .3))
#| error: false
#| warning: false
#| results: hide
plot01_mod1 <-
brm(log_effic_05righttrunc~ 1 + effsize + (1 | session),
data = study1_w_timestamp_effsize %>%
filter(plotNrWithin == 1),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 6000)
pp_check(plot01_mod1) +
theme_modern_rc() +
scale_color_manual(values=viridis(2, begin = .3))
#| error: false
#| warning: false
#| results: hide
plot01_mod2 <-
brm(log_effic_05righttrunc ~ 1 + effsize + type + (1 | session),
data = study1_w_timestamp_effsize %>%
filter(plotNrWithin == 1),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 6000)
pp_check(plot01_mod2) +
theme_modern_rc() +
scale_color_manual(values=viridis(2, begin = .3))
#| error: false
#| warning: false
#| results: hide
sjPlot::tab_model(plot01_mod0, plot01_mod1, plot01_mod2)| log_effic_05righttrunc | log_effic_05righttrunc | log_effic_05righttrunc | ||||
|---|---|---|---|---|---|---|
| Predictors | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) |
| Intercept | 10.13 | 9.97 – 10.28 | 10.12 | 9.97 – 10.29 | 10.50 | 10.34 – 10.67 |
| effsize | 0.00 | -0.03 – 0.03 | 0.00 | -0.03 – 0.03 | ||
| typehalfeye_xaxis | -0.43 | -0.48 – -0.39 | ||||
| typehalfeye_yaxis | -0.53 | -0.58 – -0.49 | ||||
| typeraincloud_yaxis | -0.50 | -0.55 – -0.46 | ||||
| Random Effects | ||||||
| σ2 | 0.25 | 0.25 | 0.20 | |||
| τ00 | 0.24 session | 0.25 session | 0.26 session | |||
| ICC | 0.49 | 0.50 | 0.56 | |||
| N | 40 session | 40 session | 40 session | |||
| Observations | 3648 | 3648 | 3648 | |||
| Marginal R2 / Conditional R2 | 0.000 / 0.458 | 0.000 / 0.458 | 0.100 / 0.558 | |||
bayes_factor(plot01_mod1, plot01_mod0)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 6Estimated Bayes factor in favor of plot01_mod1 over plot01_mod0: 0.03787bayes_factor(plot01_mod2, plot01_mod1)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 6
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5Estimated Bayes factor in favor of plot01_mod2 over plot01_mod1: 1923199344955458221287498737151986850458830681955549807694141667568390820350650397891638619778286960312023761861684420574387804479160987728791715514966605824.00000#| error: false
#| warning: false
#| results: hide
plot0306_mod0 <-
brm(log_effic_05righttrunc ~ 1 + plotNrWithin_factor + (1 | session),
data = study1_w_timestamp_effsize %>%
filter(plotNrWithin >= 4),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 6000)
pp_check(plot0306_mod0) +
theme_modern_rc() +
scale_color_manual(values=viridis(2, begin = .3))
#| error: false
#| warning: false
#| results: hide
plot0306_mod1 <-
brm(log_effic_05righttrunc ~ 1 + plotNrWithin_factor + effsize +
(1 | session),
data = study1_w_timestamp_effsize %>%
filter(plotNrWithin >= 4),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
pp_check(plot0306_mod1) +
theme_modern_rc() +
scale_color_manual(values=viridis(2, begin = .3))
#| error: false
#| warning: false
#| results: hide
plot0306_mod2 <-
brm(log_effic_05righttrunc ~ 1 + plotNrWithin_factor + effsize +
type + (1 | session),
data = study1_w_timestamp_effsize %>%
filter(plotNrWithin >= 4),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
pp_check(plot0306_mod2) +
theme_modern_rc() +
scale_color_manual(values=viridis(2, begin = .3))
sjPlot::tab_model(plot0306_mod0, plot0306_mod1, plot0306_mod2)| log_effic_05righttrunc | log_effic_05righttrunc | log_effic_05righttrunc | ||||
|---|---|---|---|---|---|---|
| Predictors | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) |
| Intercept | 9.48 | 9.35 – 9.60 | 9.48 | 9.35 – 9.61 | 9.58 | 9.45 – 9.70 |
| plotNrWithin_factor: plotNrWithin_factor5 |
-0.01 | -0.03 – 0.01 | -0.01 | -0.03 – 0.01 | -0.01 | -0.03 – 0.01 |
| plotNrWithin_factor: plotNrWithin_factor6 |
-0.19 | -0.21 – -0.17 | -0.19 | -0.21 – -0.17 | -0.19 | -0.21 – -0.17 |
| effsize | 0.00 | -0.02 – 0.02 | -0.00 | -0.02 – 0.02 | ||
| typehalfeye_xaxis | -0.13 | -0.16 – -0.11 | ||||
| typehalfeye_yaxis | -0.21 | -0.23 – -0.18 | ||||
| typeraincloud_yaxis | -0.06 | -0.09 – -0.04 | ||||
| Random Effects | ||||||
| σ2 | 0.24 | 0.24 | 0.24 | |||
| τ00 | 0.17 session | 0.17 session | 0.17 session | |||
| ICC | 0.41 | 0.41 | 0.42 | |||
| N | 40 session | 40 session | 40 session | |||
| Observations | 10944 | 10944 | 10944 | |||
| Marginal R2 / Conditional R2 | 0.019 / 0.406 | 0.019 / 0.406 | 0.034 / 0.421 | |||
bayes_factor(plot0306_mod1, plot0306_mod0)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 6
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 6Estimated Bayes factor in favor of plot0306_mod1 over plot0306_mod0: 0.02235bayes_factor(plot0306_mod2, plot0306_mod1)Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5
Iteration: 6
Iteration: 7Estimated Bayes factor in favor of plot0306_mod2 over plot0306_mod1: 19448369977027863653734927896906809075167110030193328128.00000#| results: hide
# preparing multilevel model with brms for graphical overview
# rating u3
mod_plot_ratingu3 <- brm(
scale(rating_u3) ~ (1 | type),
data = study1_w,
control = list(adapt_delta = .999),
cores = 4,
iter = 10000
)
plot_ratingu3 <- mod_plot_ratingu3 %>%
spread_draws(b_Intercept, r_type[type,]) %>%
# median_qi(type_mean = b_Intercept + r_type, .width = c(.95, .66)) %>%
mutate(type_mean = b_Intercept + r_type) %>%
ggplot(aes(y = type, x = type_mean
#xmin = .lower, xmax = .upper
)) +
# geom_pointinterval() +
stat_halfeye() +
theme_ipsum_rc() +
theme(axis.title.x = element_blank(),
axis.text.y = element_text(hjust = 0)) +
#scale_x_continuous(limits=c(-0.6, 0.6)) +
ggtitle("Accuracy in Cohen's U3 Metric")#| results: hide
# rating ov
mod_plot_ratingov <- brm (
scale(rating_ov) ~ (1 | type),
data = study1_w,
control = list(adapt_delta = .999),
cores = 4,
iter = 10000
)
plot_ratingov <- mod_plot_ratingov %>%
spread_draws(b_Intercept, r_type[type,]) %>%
# median_qi(type_mean = b_Intercept + r_type, .width = c(.95, .66)) %>%
mutate(type_mean = b_Intercept + r_type) %>%
ggplot(aes(y = type, x = type_mean
#xmin = .lower, xmax = .upper
)) +
# geom_pointinterval() +
stat_halfeye() +
theme_ipsum_rc()+
theme(axis.title.x = element_blank(),
axis.text.y = element_blank(),
axis.title.y = element_blank()) +
#scale_x_continuous(limits=c(-0.6, 0.6)) +
ggtitle('Accuracy in Overlap Metric')#| results: hide
# difficulty
mod_plot_diffi <- brm (
scale(diffi) ~ (1 | type),
data = study1_w,
control = list(adapt_delta = .999),
cores = 4,
iter = 10000
)
plot_diffi <- mod_plot_diffi %>%
spread_draws(b_Intercept, r_type[type,]) %>%
# median_qi(type_mean = b_Intercept + r_type, .width = c(.95, .66)) %>%
mutate(type_mean = b_Intercept + r_type) %>%
ggplot(aes(y = type, x = type_mean
#xmin = .lower, xmax = .upper
)) +
# geom_pointinterval() +
stat_halfeye() +
theme_ipsum_rc() +
theme(axis.title.x = element_blank(),
axis.text.y = element_blank(),
axis.title.y = element_blank()) +
#scale_x_continuous(limits=c(-0.6, 0.6)) +
ggtitle('Perceived Task Difficulty')#| results: hide
# informativity
mod_plot_infor <- brm (
scale(infor) ~ (1 | type),
data = study1_w,
control = list(adapt_delta = .999),
cores = 4,
iter = 10000
)
plot_infor <- mod_plot_infor %>%
spread_draws(b_Intercept, r_type[type,]) %>%
# median_qi(type_mean = b_Intercept + r_type, .width = c(.95, .66)) %>%
mutate(type_mean = b_Intercept + r_type) %>%
ggplot(aes(y = type, x = type_mean #xmin = .lower, xmax = .upper
)) +
# geom_pointinterval() +
stat_halfeye() +
theme_ipsum_rc() +
theme(axis.title.x = element_blank(),
axis.text.y = element_text(hjust = 0)) +
#scale_x_continuous(limits=c(-0.6, 0.6)) +
ggtitle('Perceived Informativity')#| results: hide
# value
mod_plot_value <- brm (
scale(value) ~ (1 | type),
data = study1_w,
control = list(adapt_delta = .999),
cores = 4,
iter = 10000
)
plot_value <- mod_plot_value %>%
spread_draws(b_Intercept, r_type[type,]) %>%
# median_qi(type_mean = b_Intercept + r_type, .width = c(.95, .66)) %>%
mutate(type_mean = b_Intercept + r_type) %>%
ggplot(aes(y = type, x = type_mean #xmin = .lower, xmax = .upper
)) +
# geom_pointinterval() +
stat_halfeye() +
theme_ipsum_rc() +
theme(axis.title.x = element_blank(),
axis.text.y = element_blank(),
axis.title.y = element_blank()) +
#scale_x_continuous(limits=c(-0.6, 0.6)) +
ggtitle('Perceived Value')# efficiency for the first plot
mod_plot_efficiency1 <-
brm(
scale(effic) ~ (1 | type),
data = study1_w_timestamp_effsize %>%
filter(plotNrWithin == 1),
control = list(adapt_delta = .999),
cores = 4
)
plot_efficiency1 <- mod_plot_efficiency1 %>%
spread_draws(b_Intercept, r_type[type,]) %>%
# median_qi(type_mean = b_Intercept + r_type, .width = c(.95, .66)) %>%
mutate(type_mean = b_Intercept + r_type) %>%
ggplot(aes(y = type, x = type_mean #xmin = .lower, xmax = .upper
)) +
# geom_pointinterval() +
stat_halfeye() +
theme_ipsum_rc() +
theme(axis.title.x = element_blank(),
axis.text.y = element_blank(),
axis.title.y = element_blank()) +
#scale_x_continuous(limits=c(-0.6, 0.6)) +
ggtitle('Efficiency for the First Plot')plot_contrasteffects <- plot_ratingu3 + plot_ratingov + plot_diffi + plot_infor + plot_value + plot_efficiency1 +
plot_layout(ncol=3)
plot_contrasteffects 
# ggsave("www/plot_contrasteffects.png",
# plot_contrasteffects,
# width = 25*.55,
# height = 16*.55){brms}# create dummy variables to compare visualization types for those measures that showed evidence for difference (accuracy U3, accuracy overlap, perceived task difficulty, perceived informativity, perceived value, efficiency)
## Gardner Altman Plot vs. Halfeye xaxis
gardneraltman_vs_halfeye_x <- study1_w %>%
group_by(session) %>%
mutate(dummy_gardneraltman_halfeye_x =
case_when(type == "gardneraltman_xaxis" ~ 0, TRUE ~ 1)) %>%
filter(type == "gardneraltman_xaxis" | type == "halfeye_xaxis") %>%
ungroup()
gardneraltman_vs_halfeye_x_effic <- study1_w_timestamp %>%
group_by(session) %>%
mutate(dummy_gardneraltman_halfeye_x =
case_when(type == "gardneraltman_xaxis" ~ 0, TRUE ~ 1)) %>%
filter(type == "gardneraltman_xaxis" | type == "halfeye_xaxis") %>%
ungroup() %>%
full_join(study1_w %>% select(session, effsize), by = "session")#| results: hide
mod_accu3_gardneraltman_halfeye_x <-
brm(scale(rating_u3) ~ dummy_gardneraltman_halfeye_x + effsize + (1|session),
data = gardneraltman_vs_halfeye_x%>%
filter(rating_u3_missconcept == F),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)
mod_accov_gardneraltman_halfeye_x <-
brm(scale(rating_ov) ~ dummy_gardneraltman_halfeye_x + effsize + (1|session),
data = gardneraltman_vs_halfeye_x%>%
filter(rating_ov_missconcept == F),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)#| results: hide
mod_diffi_gardneraltman_halfeye_x <-
brm(scale(diffi) ~ dummy_gardneraltman_halfeye_x + effsize
+ (1|session),
data = gardneraltman_vs_halfeye_x,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)
mod_infor_gardneraltman_halfeye_x <-
brm(scale(infor) ~ dummy_gardneraltman_halfeye_x + effsize
+ (1|session),
data = gardneraltman_vs_halfeye_x,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)mod_value_gardneraltman_halfeye_x <-
brm(scale(value) ~ dummy_gardneraltman_halfeye_x + effsize
+ (1|session),
data = gardneraltman_vs_halfeye_x,
save_pars = save_pars(all = TRUE))
mod_effic01_gardneraltman_halfeye_x <-
brm(scale(log_effic_05righttrunc) ~ dummy_gardneraltman_halfeye_x +
effsize + (1|session),
data = gardneraltman_vs_halfeye_x_effic %>%
filter(plotNrWithin == 1),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)mod_effic0306_gardneraltman_halfeye_x <-
brm(scale(log_effic_05righttrunc) ~ dummy_gardneraltman_halfeye_x +
effsize + (1|session),
data = gardneraltman_vs_halfeye_x_effic %>%
filter(plotNrWithin >= 4),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)
## Gardner Altman Plot vs. Halfeye yaxis
gardneraltman_vs_halfeye_y <- study1_w %>%
group_by(session) %>%
mutate(dummy_gardneraltman_halfeye_y =
case_when(type == "gardneraltman_xaxis" ~ 0, TRUE ~ 1)) %>%
filter(type == "gardneraltman_xaxis" | type == "halfeye_yaxis") %>%
ungroup()gardneraltman_vs_halfeye_y_effic <- study1_w_timestamp %>%
group_by(session) %>%
mutate(dummy_gardneraltman_halfeye_y =
case_when(type == "gardneraltman_xaxis" ~ 0, TRUE ~ 1)) %>%
filter(type == "gardneraltman_xaxis" | type == "halfeye_yaxis") %>%
ungroup() %>%
full_join(study1_w %>% select(session, effsize), by = "session")
mod_accu3_gardneraltman_halfeye_y <-
brm(scale(rating_u3) ~ dummy_gardneraltman_halfeye_y + effsize + (1|session),
data = gardneraltman_vs_halfeye_y%>%
filter(rating_u3_missconcept == F),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)mod_accov_gardneraltman_halfeye_y <-
brm(scale(rating_ov) ~ dummy_gardneraltman_halfeye_y + effsize + (1|session),
data = gardneraltman_vs_halfeye_y%>%
filter(rating_ov_missconcept == F),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
mod_diffi_gardneraltman_halfeye_y <-
brm(scale(diffi) ~ dummy_gardneraltman_halfeye_y + effsize
+ (1|session),
data = gardneraltman_vs_halfeye_y,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)mod_infor_gardneraltman_halfeye_y <-
brm(scale(infor) ~ dummy_gardneraltman_halfeye_y + effsize
+ (1|session),
data = gardneraltman_vs_halfeye_y,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
mod_value_gardneraltman_halfeye_y <-
brm(scale(value) ~ dummy_gardneraltman_halfeye_y + effsize
+ (1|session),
data = gardneraltman_vs_halfeye_y,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)mod_effic01_gardneraltman_halfeye_y <-
brm(scale(log_effic_05righttrunc) ~ dummy_gardneraltman_halfeye_y +
effsize + (1|session),
data = gardneraltman_vs_halfeye_y_effic %>%
filter(plotNrWithin == 1),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
mod_effic0306_gardneraltman_halfeye_y <-
brm(scale(log_effic_05righttrunc) ~ dummy_gardneraltman_halfeye_y +
effsize + (1|session),
data = gardneraltman_vs_halfeye_y_effic %>%
filter(plotNrWithin >= 4),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)## Raincloud Plot vs. Halfeye x axis
raincloud_vs_halfeye_x <- study1_w %>%
group_by(session) %>%
mutate(dummy_raincloud_halfeye_x =
case_when(type == "raincloud_yaxis" ~ 0, TRUE ~ 1)) %>%
filter(type == "halfeye_xaxis" | type == "raincloud_yaxis") %>%
ungroup()
raincloud_vs_halfeye_x_effic <- study1_w_timestamp %>%
group_by(session) %>%
mutate(dummy_raincloud_halfeye_x =
case_when(type == "raincloud_yaxis" ~ 0, TRUE ~ 1)) %>%
filter(type == "raincloud_yaxis" | type == "halfeye_xaxis") %>%
ungroup() %>%
full_join(study1_w %>% select(session, effsize), by = "session")mod_accu3_raincloud_halfeye_x <-
brm(scale(rating_u3) ~ dummy_raincloud_halfeye_x + effsize + (1|session),
data = raincloud_vs_halfeye_x %>%
filter(rating_u3_missconcept == F),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
mod_accov_raincloud_halfeye_x <-
brm(scale(rating_ov) ~ dummy_raincloud_halfeye_x + effsize + (1|session),
data = raincloud_vs_halfeye_x %>%
filter(rating_ov_missconcept == F),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)mod_diffi_raincloud_halfeye_x <-
brm(scale(diffi) ~ dummy_raincloud_halfeye_x +
effsize + (1|session),
data = raincloud_vs_halfeye_x,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
mod_infor_raincloud_halfeye_x <-
brm(scale(infor) ~ dummy_raincloud_halfeye_x +
effsize + (1|session),
data = raincloud_vs_halfeye_x,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)mod_value_raincloud_halfeye_x <-
brm(scale(value) ~ dummy_raincloud_halfeye_x +
effsize + (1|session),
data = raincloud_vs_halfeye_x,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
mod_effic01_raincloud_halfeye_x <-
brm(scale(log_effic_05righttrunc) ~ dummy_raincloud_halfeye_x +
effsize + (1|session),
data = raincloud_vs_halfeye_x_effic %>%
filter(plotNrWithin == 1),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)mod_effic0306_raincloud_halfeye_x <-
brm(scale(log_effic_05righttrunc) ~ dummy_raincloud_halfeye_x +
effsize + (1|session),
data = raincloud_vs_halfeye_x_effic %>%
filter(plotNrWithin >= 4),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)## Raincloud Plot vs.Halfeye yaxis
raincloud_vs_halfeye_y <- study1_w %>%
group_by(session) %>%
mutate(dummy_raincloud_halfeye_y =
case_when(type == "raincloud_yaxis" ~ 0, TRUE ~ 1)) %>%
filter(type == "halfeye_yaxis" | type == "raincloud_yaxis") %>%
ungroup()
raincloud_vs_halfeye_y_effic <- study1_w_timestamp %>%
group_by(session) %>%
mutate(dummy_raincloud_halfeye_y =
case_when(type == "raincloud_yaxis" ~ 0, TRUE ~ 1)) %>%
filter(type == "raincloud_yaxis" | type == "halfeye_yaxis") %>%
ungroup() %>%
full_join(study1_w %>% select(session, effsize), by = "session")mod_accu3_raincloud_halfeye_y <-
brm(scale(rating_u3) ~ dummy_raincloud_halfeye_y + effsize + (1|session),
data = raincloud_vs_halfeye_y %>%
filter(rating_u3_missconcept == F),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
mod_accov_raincloud_halfeye_y <-
brm(scale(rating_ov) ~ dummy_raincloud_halfeye_y + effsize + (1|session),
data = raincloud_vs_halfeye_y %>%
filter(rating_ov_missconcept == F),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)mod_diffi_raincloud_halfeye_y <-
brm(scale(diffi) ~ dummy_raincloud_halfeye_y +
effsize + (1|session),
data = raincloud_vs_halfeye_y,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
mod_infor_raincloud_halfeye_y <-
brm(scale(infor) ~ dummy_raincloud_halfeye_y +
effsize + (1|session),
data = raincloud_vs_halfeye_y,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)mod_value_raincloud_halfeye_y <-
brm(scale(value) ~ dummy_raincloud_halfeye_y +
effsize + (1|session),
data = raincloud_vs_halfeye_y,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
mod_effic01_raincloud_halfeye_y <-
brm(scale(log_effic_05righttrunc) ~ dummy_raincloud_halfeye_y +
effsize + (1|session),
data = raincloud_vs_halfeye_y_effic %>%
filter(plotNrWithin == 1),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)
mod_effic0306_raincloud_halfeye_y <-
brm(scale(log_effic_05righttrunc) ~ dummy_raincloud_halfeye_y +
effsize + (1|session),
data = raincloud_vs_halfeye_y_effic %>%
filter(plotNrWithin >= 4),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)sjPlot::tab_model(mod_accu3_gardneraltman_halfeye_x,
mod_accov_gardneraltman_halfeye_x, mod_diffi_gardneraltman_halfeye_x,
mod_infor_gardneraltman_halfeye_x, mod_value_gardneraltman_halfeye_x,
mod_effic01_gardneraltman_halfeye_x, mod_effic0306_gardneraltman_halfeye_x,
mod_accu3_gardneraltman_halfeye_y, mod_accov_gardneraltman_halfeye_y,
mod_diffi_gardneraltman_halfeye_y, mod_infor_gardneraltman_halfeye_y,
mod_value_gardneraltman_halfeye_y,
mod_effic01_gardneraltman_halfeye_y, mod_effic0306_gardneraltman_halfeye_y,
mod_accov_raincloud_halfeye_x, mod_diffi_raincloud_halfeye_x,
mod_infor_raincloud_halfeye_x, mod_value_raincloud_halfeye_x,
mod_effic01_raincloud_halfeye_x, mod_effic0306_raincloud_halfeye_x,
mod_accu3_raincloud_halfeye_y, mod_accov_raincloud_halfeye_y,
mod_diffi_raincloud_halfeye_y, mod_infor_raincloud_halfeye_y,
mod_value_raincloud_halfeye_y,
mod_effic01_raincloud_halfeye_y, mod_effic0306_raincloud_halfeye_y)| scale(rating_u3) | scale(rating_ov) | scale(diffi) | scale(infor) | scale(value) | scale(log_effic_05righttrunc) | scale(log_effic_05righttrunc) | scale(rating_u3) | scale(rating_ov) | scale(diffi) | scale(infor) | scale(value) | scale(log_effic_05righttrunc) | scale(log_effic_05righttrunc) | scale(rating_ov) | scale(diffi) | scale(infor) | scale(value) | scale(log_effic_05righttrunc) | scale(log_effic_05righttrunc) | scale(rating_u3) | scale(rating_ov) | scale(diffi) | scale(infor) | scale(value) | scale(log_effic_05righttrunc) | scale(log_effic_05righttrunc) | ||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) |
| Intercept | -0.00 | -0.35 – 0.33 | -0.11 | -0.35 – 0.13 | -0.19 | -0.44 – 0.06 | -0.13 | -0.39 – 0.12 | -0.17 | -0.43 – 0.09 | 0.34 | 0.10 – 0.58 | 0.10 | -0.10 – 0.30 | -0.17 | -0.49 – 0.17 | 0.01 | -0.25 – 0.28 | -0.28 | -0.50 – -0.03 | -0.20 | -0.42 – 0.02 | -0.20 | -0.44 – 0.04 | 0.41 | 0.15 – 0.64 | 0.17 | -0.04 – 0.37 | -0.11 | -0.34 – 0.10 | -0.12 | -0.35 – 0.12 | -0.05 | -0.28 – 0.17 | -0.09 | -0.33 – 0.16 | -0.05 | -0.33 – 0.24 | 0.07 | -0.17 – 0.29 | -0.16 | -0.45 – 0.15 | 0.04 | -0.20 – 0.27 | -0.22 | -0.45 – 0.00 | -0.13 | -0.36 – 0.10 | -0.11 | -0.35 – 0.12 | 0.05 | -0.24 – 0.34 | 0.14 | -0.10 – 0.37 |
| dummy_gardneraltman_halfeye_x | 0.04 | -0.27 – 0.35 | 0.18 | -0.11 – 0.47 | 0.37 | 0.25 – 0.49 | 0.26 | 0.14 – 0.39 | 0.35 | 0.24 – 0.46 | -0.69 | -0.75 – -0.64 | -0.20 | -0.24 – -0.16 | ||||||||||||||||||||||||||||||||||||||||
| effsize | -0.72 | -1.00 – -0.44 | 0.04 | -0.22 – 0.31 | -0.04 | -0.15 – 0.06 | -0.02 | -0.13 – 0.08 | -0.05 | -0.16 – 0.05 | -0.00 | -0.05 – 0.05 | 0.00 | -0.04 – 0.04 | -0.50 | -0.81 – -0.17 | -0.08 | -0.33 – 0.18 | -0.03 | -0.15 – 0.07 | -0.03 | -0.15 – 0.09 | -0.02 | -0.12 – 0.09 | 0.00 | -0.04 – 0.04 | 0.00 | -0.04 – 0.04 | -0.07 | -0.34 – 0.18 | -0.07 | -0.19 – 0.05 | -0.06 | -0.19 – 0.07 | -0.05 | -0.16 – 0.06 | -0.00 | -0.05 – 0.05 | -0.00 | -0.04 – 0.04 | -0.53 | -0.84 – -0.23 | -0.15 | -0.40 – 0.10 | -0.06 | -0.18 – 0.05 | -0.06 | -0.19 – 0.06 | -0.01 | -0.13 – 0.10 | -0.00 | -0.04 – 0.04 | 0.00 | -0.03 – 0.03 |
| dummy_gardneraltman_halfeye_y | 0.29 | -0.05 – 0.65 | -0.03 | -0.31 – 0.23 | 0.54 | 0.41 – 0.66 | 0.40 | 0.27 – 0.54 | 0.40 | 0.27 – 0.51 | -0.79 | -0.83 – -0.74 | -0.32 | -0.37 – -0.28 | ||||||||||||||||||||||||||||||||||||||||
| dummy_raincloud_halfeye_x | 0.21 | -0.07 – 0.50 | 0.25 | 0.12 – 0.37 | 0.10 | -0.03 – 0.24 | 0.19 | 0.06 – 0.32 | 0.15 | 0.10 – 0.20 | -0.12 | -0.16 – -0.08 | ||||||||||||||||||||||||||||||||||||||||||
| dummy_raincloud_halfeye_y | 0.35 | -0.00 – 0.70 | -0.06 | -0.34 – 0.24 | 0.43 | 0.30 – 0.57 | 0.25 | 0.11 – 0.39 | 0.24 | 0.11 – 0.36 | -0.00 | -0.05 – 0.05 | -0.23 | -0.27 – -0.19 | ||||||||||||||||||||||||||||||||||||||||
| Random Effects | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
| σ2 | 0.64 | 0.95 | 0.43 | 0.46 | 0.40 | 0.35 | 0.58 | 0.78 | 0.83 | 0.46 | 0.55 | 0.47 | 0.25 | 0.58 | 0.98 | 0.55 | 0.62 | 0.50 | 0.31 | 0.54 | 0.79 | 0.92 | 0.54 | 0.60 | 0.54 | 0.26 | 0.51 | |||||||||||||||||||||||||||
| τ00 | 0.27 session | 0.07 session | 0.59 session | 0.57 session | 0.63 session | 0.60 session | 0.43 session | 0.21 session | 0.21 session | 0.51 session | 0.44 session | 0.54 session | 0.71 session | 0.42 session | 0.04 session | 0.48 session | 0.41 session | 0.54 session | 0.79 session | 0.50 session | 0.14 session | 0.10 session | 0.45 session | 0.42 session | 0.49 session | 0.86 session | 0.53 session | |||||||||||||||||||||||||||
| ICC | 0.30 | 0.07 | 0.58 | 0.55 | 0.62 | 0.63 | 0.42 | 0.21 | 0.20 | 0.52 | 0.45 | 0.53 | 0.74 | 0.42 | 0.04 | 0.47 | 0.40 | 0.52 | 0.72 | 0.48 | 0.15 | 0.10 | 0.46 | 0.41 | 0.48 | 0.77 | 0.51 | |||||||||||||||||||||||||||
| N | 18 session | 31 session | 40 session | 40 session | 40 session | 39 session | 40 session | 18 session | 31 session | 40 session | 40 session | 40 session | 40 session | 40 session | 31 session | 40 session | 40 session | 40 session | 39 session | 40 session | 18 session | 31 session | 40 session | 40 session | 40 session | 40 session | 40 session | |||||||||||||||||||||||||||
| Observations | 109 | 185 | 480 | 480 | 480 | 1824 | 5472 | 104 | 177 | 480 | 480 | 480 | 1824 | 5472 | 195 | 480 | 480 | 480 | 1824 | 5472 | 107 | 187 | 480 | 480 | 480 | 1824 | 5472 | |||||||||||||||||||||||||||
| Marginal R2 / Conditional R2 | 0.167 / 0.392 | 0.014 / 0.080 | 0.036 / 0.579 | 0.018 / 0.543 | 0.032 / 0.608 | 0.118 / 0.654 | 0.010 / 0.418 | 0.106 / 0.268 | 0.008 / 0.201 | 0.074 / 0.541 | 0.042 / 0.452 | 0.040 / 0.534 | 0.151 / 0.752 | 0.027 / 0.419 | 0.018 / 0.055 | 0.018 / 0.460 | 0.005 / 0.384 | 0.011 / 0.509 | 0.006 / 0.688 | 0.004 / 0.461 | 0.130 / 0.255 | 0.013 / 0.111 | 0.049 / 0.467 | 0.018 / 0.408 | 0.015 / 0.471 | 0.000 / 0.744 | 0.013 / 0.488 | |||||||||||||||||||||||||||
## Gardner Altman Plot vs. Raincloud Plot
gardneraltman_vs_raincloud <- study1_w %>%
group_by(session) %>%
mutate(dummy_gardneraltman_raincloud =
case_when(type == "gardneraltman_xaxis" ~ 0, TRUE ~ 1)) %>%
filter(type == "gardneraltman_xaxis" | type == "raincloud_yaxis") %>%
ungroup()
gardneraltman_vs_raincloud_effic <- study1_w_timestamp %>%
group_by(session) %>%
mutate(dummy_gardneraltman_raincloud =
case_when(type == "gardneraltman_xaxis" ~ 0, TRUE ~ 1)) %>%
filter(type == "gardneraltman_xaxis" | type == "raincloud_yaxis") %>%
ungroup() %>%
full_join(study1_w %>% select(session, effsize), by = "session")mod_accu3_gardneraltman_raincloud <-
brm(scale(rating_u3) ~ dummy_gardneraltman_raincloud + effsize + (1|session),
data = gardneraltman_vs_raincloud%>%
filter(rating_u3_missconcept == F),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
mod_accov_gardneraltman_raincloud <-
brm(scale(rating_ov) ~ dummy_gardneraltman_raincloud + effsize + (1|session),
data = gardneraltman_vs_raincloud%>%
filter(rating_ov_missconcept == F),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)mod_diffi_gardneraltman_raincloud <-
brm(scale(diffi) ~ dummy_gardneraltman_raincloud +
effsize + (1|session),
data = gardneraltman_vs_raincloud,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
mod_infor_gardneraltman_raincloud <-
brm(scale(infor) ~ dummy_gardneraltman_raincloud +
effsize + (1|session),
data = gardneraltman_vs_raincloud,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)mod_value_gardneraltman_raincloud <-
brm(scale(value) ~ dummy_gardneraltman_raincloud +
effsize + (1|session),
data = gardneraltman_vs_raincloud,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
mod_effic01_gardneraltman_raincloud <-
brm(scale(log_effic_05righttrunc) ~ dummy_gardneraltman_raincloud +
effsize + (1|session),
data = gardneraltman_vs_raincloud_effic %>%
filter(plotNrWithin == 1),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)
mod_effic0306_gardneraltman_raincloud <-
brm(scale(log_effic_05righttrunc) ~ dummy_gardneraltman_raincloud +
effsize + (1|session),
data = gardneraltman_vs_raincloud_effic %>%
filter(plotNrWithin >= 4),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)## Halfeye xaxis vs. Halfeye yaxis
halfeye_x_vs_halfeye_y <- study1_w %>%
group_by(session) %>%
mutate(dummy_halfeye_x_halfeye_y =
case_when(type == "halfeye_xaxis" ~ 0, TRUE ~ 1)) %>%
filter(type == "halfeye_xaxis" | type == "halfeye_yaxis") %>%
ungroup()
halfeye_x_vs_halfeye_y_effic <- study1_w_timestamp %>%
group_by(session) %>%
mutate(dummy_halfeye_x_halfeye_y =
case_when(type == "halfeye_xaxis" ~ 0, TRUE ~ 1)) %>%
filter(type == "halfeye_xaxis" | type == "halfeye_yaxis") %>%
ungroup() %>%
full_join(study1_w %>% select(session, effsize), by = "session")mod_accu3_halfeye_x_halfeye_y <-
brm(scale(rating_u3) ~ dummy_halfeye_x_halfeye_y +
effsize + (1|session),
data = halfeye_x_vs_halfeye_y %>%
filter(rating_u3_missconcept == F),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)
mod_accov_halfeye_x_halfeye_y <-
brm(scale(rating_ov) ~ dummy_halfeye_x_halfeye_y +
effsize + (1|session),
data = halfeye_x_vs_halfeye_y%>%
filter(rating_ov_missconcept == F),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 20000)mod_diffi_halfeye_x_halfeye_y <-
brm(scale(diffi) ~ dummy_halfeye_x_halfeye_y +
effsize + (1|session),
data = halfeye_x_vs_halfeye_y,
iter = 10000,
save_pars = save_pars(all = TRUE))
mod_infor_halfeye_x_halfeye_y <-
brm(scale(infor) ~ dummy_halfeye_x_halfeye_y +
effsize + (1|session),
data = halfeye_x_vs_halfeye_y,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4)mod_value_halfeye_x_halfeye_y <-
brm(scale(value) ~ dummy_halfeye_x_halfeye_y +
effsize + (1|session),
data = halfeye_x_vs_halfeye_y,
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)
mod_effic01_halfeye_x_halfeye_y <-
brm(scale(log_effic_05righttrunc) ~ dummy_halfeye_x_halfeye_y +
effsize + (1|session),
data = halfeye_x_vs_halfeye_y_effic %>%
filter(plotNrWithin == 1),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
iter = 10000)mod_effic0306_halfeye_x_halfeye_y <-
brm(scale(log_effic_05righttrunc) ~ dummy_halfeye_x_halfeye_y +
effsize + (1|session),
data = halfeye_x_vs_halfeye_y_effic%>%
filter(plotNrWithin >= 4),
save_pars = save_pars(all = TRUE),
silent = 2,
refresh = 0,
cores = 4,
iter = 10000)sjPlot::tab_model(mod_accu3_halfeye_x_halfeye_y, mod_accov_halfeye_x_halfeye_y,
mod_diffi_halfeye_x_halfeye_y, mod_infor_halfeye_x_halfeye_y,
mod_value_halfeye_x_halfeye_y,
mod_effic01_halfeye_x_halfeye_y)| scale(rating_u3) | scale(rating_ov) | scale(diffi) | scale(infor) | scale(value) | scale(log_effic_05righttrunc) | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) |
| Intercept | -0.10 | -0.43 – 0.23 | 0.11 | -0.10 – 0.32 | -0.10 | -0.35 – 0.15 | -0.08 | -0.34 – 0.17 | -0.03 | -0.29 – 0.23 | 0.11 | -0.17 – 0.38 |
| dummy_halfeye_x_halfeye_y | 0.17 | -0.16 – 0.49 | -0.23 | -0.51 – 0.05 | 0.20 | 0.08 – 0.33 | 0.16 | 0.04 – 0.28 | 0.05 | -0.06 – 0.16 | -0.14 | -0.20 – -0.08 |
| effsize | -0.68 | -0.97 – -0.37 | -0.04 | -0.31 – 0.22 | -0.01 | -0.12 – 0.10 | 0.03 | -0.08 – 0.15 | 0.05 | -0.05 – 0.16 | -0.00 | -0.05 – 0.05 |
| Random Effects | ||||||||||||
| σ2 | 0.70 | 0.98 | 0.48 | 0.47 | 0.41 | 0.37 | ||||||
| τ00 | 0.25 session | 0.03 session | 0.55 session | 0.57 session | 0.65 session | 0.72 session | ||||||
| ICC | 0.26 | 0.03 | 0.53 | 0.55 | 0.62 | 0.66 | ||||||
| N | 18 session | 31 session | 40 session | 40 session | 40 session | 39 session | ||||||
| Observations | 109 | 194 | 480 | 480 | 480 | 1824 | ||||||
| Marginal R2 / Conditional R2 | 0.151 / 0.334 | 0.018 / 0.052 | 0.012 / 0.521 | 0.008 / 0.538 | 0.002 / 0.598 | 0.005 / 0.626 | ||||||
sjPlot::tab_model(mod_effic0306_halfeye_x_halfeye_y,
mod_accu3_gardneraltman_raincloud,
mod_accov_gardneraltman_raincloud,
mod_diffi_gardneraltman_raincloud)| scale(log_effic_05righttrunc) | scale(rating_u3) | scale(rating_ov) | scale(diffi) | |||||
|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) |
| Intercept | 0.08 | -0.16 – 0.30 | 0.13 | -0.22 – 0.47 | 0.01 | -0.23 – 0.26 | -0.06 | -0.29 – 0.17 |
| dummy_halfeye_x_halfeye_y | -0.12 | -0.16 – -0.09 | ||||||
| effsize | -0.00 | -0.03 – 0.03 | -0.69 | -0.97 – -0.40 | -0.10 | -0.36 – 0.14 | -0.09 | -0.22 – 0.03 |
| dummy_gardneraltman_raincloud | -0.18 | -0.52 – 0.15 | -0.01 | -0.31 – 0.27 | 0.13 | 0.00 – 0.27 | ||
| Random Effects | ||||||||
| σ2 | 0.52 | 0.68 | 0.89 | 0.59 | ||||
| τ00 | 0.53 session | 0.24 session | 0.15 session | 0.44 session | ||||
| ICC | 0.51 | 0.26 | 0.14 | 0.43 | ||||
| N | 40 session | 18 session | 31 session | 40 session | ||||
| Observations | 5472 | 107 | 178 | 480 | ||||
| Marginal R2 / Conditional R2 | 0.004 / 0.482 | 0.161 / 0.357 | 0.010 / 0.149 | 0.009 / 0.420 | ||||
sjPlot::tab_model(mod_infor_gardneraltman_raincloud,
mod_value_gardneraltman_raincloud,
mod_effic01_gardneraltman_raincloud,
mod_effic0306_gardneraltman_raincloud)| scale(infor) | scale(value) | scale(log_effic_05righttrunc) | scale(log_effic_05righttrunc) | |||||
|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) | Estimates | CI (95%) |
| Intercept | -0.08 | -0.31 – 0.14 | -0.07 | -0.33 – 0.18 | 0.41 | 0.12 – 0.67 | 0.05 | -0.16 – 0.25 |
| dummy_gardneraltman_raincloud | 0.16 | 0.02 – 0.29 | 0.15 | 0.01 – 0.28 | -0.77 | -0.81 – -0.72 | -0.10 | -0.14 – -0.06 |
| effsize | -0.11 | -0.24 – 0.01 | -0.11 | -0.23 – 0.00 | 0.00 | -0.04 – 0.04 | -0.00 | -0.04 – 0.04 |
| Random Effects | ||||||||
| σ2 | 0.60 | 0.50 | 0.25 | 0.57 | ||||
| τ00 | 0.43 session | 0.54 session | 0.71 session | 0.45 session | ||||
| ICC | 0.42 | 0.52 | 0.74 | 0.44 | ||||
| N | 40 session | 40 session | 39 session | 40 session | ||||
| Observations | 480 | 480 | 1824 | 5472 | ||||
| Marginal R2 / Conditional R2 | 0.011 / 0.407 | 0.010 / 0.506 | 0.144 / 0.747 | 0.003 / 0.426 | ||||